Titlebook: Grade Models and Methods for Data Analysis; With Applications fo Teresa Kowalczyk,El?bieta Pleszczyńska,Frederick R Book 2004 Springer-Verl

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Discretization and regularity,ion of two variables is considered. Due to the formalism used (i.e., grade framework) which is based on concentration indices, the discretization of one random variable can be treated as a particular example of this problem. The grade framework is described in Sec. 6.2.

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Preliminary concepts of bivariate dependence,istributions. Traditional infrastructure (in particular, regression and correlation meaningful for at least interval variables) is completed with parameters based on concentration measures (applied e.g., to pairs of conditional distributions). In this way concepts belonging to the Univariate Lillipu

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Cluster analysis based on GCA,escribed in Chapter 6 for pairs of random variables. Although the theoretical background assumes that the data must have the form of two-way probability tables, the method is applicable to almost any two-dimensional table which contains non-negative values. For example, input data tables can contain

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Regularity and the number of clusters, curves introduced in Chapter 6. It will be shown that members of a parametrized family of two-dimensional distributions from the Bivariate Lilliputian Model (???)which are based on parabolas are ideally regular data tables (Sections 11.2–11.3).

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Inequality measures for multivariate distributions,variate datasets with . sets of records. These measures are easy to percept for those who are familiar with the concepts of the Lorenz Curve and Gini Index which are so important in the univariate case.

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https://doi.org/10.1007/978-94-009-5145-7s, data were ordered according to increasing earnings. For earnings aggregated to four macrocategories, the concentration curve .(. : .) relating male earnings . to female earnings . happened to be convex. This indicated a clear monotone trend: the higher the earnings macrocategory, the stronger the

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https://doi.org/10.1007/978-1-4684-5203-7ack sufficient precision. It is due to the fact that there are many curves which have the same index value. Problems related to how greatly such curves may differ are discussed in Sec. 5.2. In particular, the boundaries of the smallest region containing all maximal concentration curves with a common

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